Novel and Emerging Perspectives in Health Policy and Care in Indigenous and Tribal Populations: A Bibliometric Analysis
Bibliographic record
Abstract
ABSTRACT Tribal and indigenous communities around the world possess rich and diverse cultural traditions. Despite this, they continue to experience considerable health inequalities, largely due to past exclusion, inadequate healthcare access, and socio‐economic challenges. Recently, there has been a growing academic focus on formulating inclusive health policies and enhancing healthcare systems to improve health outcomes and ensure equity for these populations. This study uses a bibliometric analysis of the Scopus database to examine research on indigenous and tribal health policy and healthcare from 1988 to 2024. From an initial 326 articles, a final dataset of 265 articles was curated. The findings offer a comprehensive bibliometric analysis of health policy and care for indigenous and tribal populations. Australia is the leading contributor, collaborating with countries like Canada, the USA, and New Zealand. Universities such as the University of Sydney and Flinders University are pivotal in advancing research on the subject. In science mapping, co‐word analysis identifies emerging trends followed by bibliographic coupling which discovers promising themes. This analysis informs strategies to enhance global health outcomes for indigenous and tribal populations through interdisciplinary research, collaboration, and effective policy interventions, aiming for equitable and culturally responsive healthcare solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.180 | 0.268 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".